VLMEvalKit / .github /scripts /assert_score.py
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import argparse
import ast
import json
import os
import pandas as pd
def validate_scores(dataset_list, assert_score, model_name):
for dataset in dataset_list:
base_score = assert_score[dataset][model_name]
if dataset == "OCRBench_MINI":
score_file = os.path.join("outputs", f"{model_name}/{model_name}_{dataset}_score.json")
cur_score = 0
with open(score_file, "r") as f:
total_score = json.load(f)
cur_score = total_score["Final Score Norm"]
assert (
abs(cur_score - float(base_score)) <= 0.01
), f"{dataset} on {model_name}: cur_score is {cur_score}, base_score is {base_score}"
else:
score_file = os.path.join("outputs", f"{model_name}/{model_name}_{dataset}_acc.csv")
df = pd.read_csv(score_file)
cur_score = df["Overall"].iloc[0]
if dataset == "MMBench_V11_MINI":
cur_score = df.loc[df["split"] == "dev", "Overall"].values
assert (
abs(cur_score - float(base_score)) <= 0.01
), f"{dataset} on {model_name}: cur_score is {cur_score}, base_score is {base_score}"
print(f"cur_score is {cur_score}, base_score is {base_score}")
def parse_arguments():
parser = argparse.ArgumentParser(description="Validate model scores against csv/json data")
parser.add_argument("--dataset", type=str, required=True, help="Space-separated list of datasets")
parser.add_argument(
"--base_score", type=str, required=True, help="Dictionary string in format {dataset:{model:score}}"
)
parser.add_argument("--model-name", type=str, required=True, help="Name of the model to validate")
return parser.parse_args()
def main():
args = parse_arguments()
try:
dataset_list = args.dataset.split()
base_score = ast.literal_eval(args.base_score)
except Exception as e:
print(f"Parameter parsing error: {str(e)}")
return
validate_scores(dataset_list, base_score, args.model_name)
if __name__ == "__main__":
main()